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MindGrab: A spectrally-motivated architecture for accessible deep learning in neuroimaging.

Armina Fani1, Mike Doan1, Isabelle Le1

  • 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, GA, 30303, USA.

Neuroimage
|June 22, 2026
PubMed
Summary

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MindGrab is a new deep learning model for fast and accurate brain image skull stripping. It offers state-of-the-art performance with significantly reduced computational demands, making neuroimaging analysis more accessible.

Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Image Analysis

Background:

  • Deep learning models for neuroimaging face adoption barriers due to deployment complexity and hardware needs.
  • Volumetric skull stripping is a crucial preprocessing step in neuroimaging analysis.

Purpose of the Study:

  • To introduce MindGrab, a lightweight, fully convolutional model for efficient volumetric skull stripping.
  • To demonstrate MindGrab's state-of-the-art performance and accessibility across various neuroimaging modalities.

Main Methods:

  • Developed a novel lightweight, fully convolutional neural network architecture (MindGrab) using a spectral interpretation of dilated convolutions.
  • Evaluated MindGrab's performance on multiple neuroimaging datasets and modalities, comparing it against established skull stripping methods.
Keywords:
Deep learningDilated convolutionsNeuroimagingOmnimodalSkull strippingZero footprint AI

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  • Assessed computational efficiency, including speedups and memory demands.

    • Main Results:

    • MindGrab achieved state-of-the-art performance with a mean Dice score of 95.9 ± 1.6 across datasets and modalities.
    • Demonstrated up to 40-fold speedups and substantially lower memory requirements compared to existing methods.
    • Enabled fast, full-volume processing in resource-constrained environments, including direct in-browser execution.

    Conclusions:

    • MindGrab effectively removes traditional deployment barriers for deep learning in neuroimaging without compromising accuracy.
    • The model's minimal footprint and high efficiency make advanced neuroimaging analysis broadly accessible.
    • MindGrab is available as a command-line tool and a web application via the BrainChop platform.